A map (FIG. 1) is a collection of measurement points defined by any number of measurements associated with geographical coordinates.
In the case of the present patent a map is defined as the combination of all the measurements (observation data) ordered so as to cover a given geographical zone or any surface described by a set of measurements referenced by spatial coordinates with from 1 to 3 dimensions.
For example, a conventional geographical map is a series of measurements transferred onto a two-dimensional plane surface (this involves a procedure for projecting a set of three-dimensional measurements onto a plane surface, because the Earth is not flat).
In the context of the invention, what is of interest is how a set of measurements collected by mobile sensors (persons, vehicles, aircraft or satellites) can be collected together as effectively and as economically as possible, in particular when the quantity of measurements collected becomes very large and the mobile sensors are disposed over a very large area.
If what is of interest is mapping the terrestrial globe as a whole and with a high refresh rate, for example, the analysis of the problem typically leads to practical impossibilities or to processing loads that render capture, for example of the whole of the planet, impracticable in the current state of the art.
For example: a color image of the globe at a sample resolution of the order of 33 cm (which is the resolution of many classic airborne imaging systems) corresponds to: 5.1×1014 m2 i.e. approximately 5×1015 samples. Assuming that each of the measurement samples is coded with three colors each on 256 levels (coded on three bytes), this represents in all approximately 1.5×1016 bytes to be refreshed daily, which no present day computer centre knows how to process within an acceptable time frame and/or at an acceptable cost.
It is absolutely impossible to manage this amount of data using classic computation means. Not only because of the large volume involved but also and above all because of the impossibility of repatriating this information in a given place, given that it is acquired by sensors that can be moving over the whole of the planet in some applications.
The paper by Y. M. Teo et al. entitled “Distributed geo-rectification of satellite images using Grid computing” in PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM, 2003. PROCEEDINGS INTERNATIONAL, Apr. 22-26, 2003, PISCATAWAY, N.J., USA, IEEE, (2003 Apr. 22), pages 15-22, XP010645301, ISBN: 978-0-7695-1926-5, describes a classic example of the application of parallel processing in a GRID, and therefore predictably and deterministically.
The method described in the above paper necessitates the use of two distinct mechanisms including a “Task Generator” that must absolutely have an exact and deterministic knowledge of the breakdown created (otherwise it will not be possible to group the information together) and a “Result Collector”, which must have a perfect and deterministic knowledge of the breakdowns effected by the Task Generator.